SAVIOR: Securing Autonomous Vehicles with Robust Physical Invariants

Authors: 

Raul Quinonez, University of Texas at Dallas; Jairo Giraldo, University of Utah; Luis Salazar, University of California, Santa Cruz; Erick Bauman, University of Texas at Dallas; Alvaro Cardenas, University of California, Santa Cruz; Zhiqiang Lin, Ohio State University

Abstract: 

Autonomous Vehicles (AVs), including aerial, sea, and ground vehicles, assess their environment with a variety of sensors and actuators that allow them to perform specific tasks such as navigating a route, hovering, or avoiding collisions. So far, AVs tend to trust the information provided by their sensors to make navigation decisions without data validation or verification, and therefore, attackers can exploit these limitations by feeding erroneous sensor data with the intention of disrupting or taking control of the system. In this paper we introduce SAVIOR: an architecture for securing autonomous vehicles with robust physical invariants. We implement and validate our proposal on two popular open-source controllers for aerial and ground vehicles, and demonstrate its effectiveness.

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BibTeX
@inproceedings {247688,
title = {{SAVIOR}: Securing Autonomous Vehicles with Robust Physical Invariants},
booktitle = {29th {USENIX} Security Symposium ({USENIX} Security 20)},
year = {2020},
address = {Boston, MA},
url = {https://www.usenix.org/conference/usenixsecurity20/presentation/quinonez},
publisher = {{USENIX} Association},
month = aug,
}